Bibliographic record
Abstract
This paper embarks on a classical scientific journey to explore and elucidate the intricate world of research cognition and methods. Delving into quantitative and qualitative methodologies, highlighting their features, and dispels misconceptions and erroneous assumptions surrounding them. The discourse extends to examining the pros and cons of these approaches, clarifying terminology related to case studies and population size. The work advocates for revisions in referencing styles and argues against the use of a, b, c format in in-text citations. The paper also clarifies why experimental research cannot be conducted by social sciences due to its inherently limited control power features, compared to the natural sciences. Through the navigation of this path, the ultimate goal is to demystify research methodologies and cultivate a deeper comprehension of scientific language
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.177 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.090 |
| Scholarly communication | 0.034 | 0.084 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.017 | 0.047 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".